53 citations · 143 across the 24 of their papers we have counts for
8 papers · 1 filter
Generalized system identification with stable spline kernels
Aleksandr Y. Aravkin, James V. Burke, Gianluigi Pillonetto
Regularized least-squares approaches have been successfully applied to linear system identification. Recent approaches use quadratic penalty terms on the unknown impulse response d…
Robust and Trend Following Student's t Kalman Smoothers
Aleksandr Y. Aravkin, James V. Burke, Gianluigi Pillonetto
We present a Kalman smoothing framework based on modeling errors using the heavy tailed Student's t distribution, along with algorithms, convergence theory, open-source general imp…
Linear system identification using stable spline kernels and PLQ penalties
Aleksandr Y. Aravkin, James V. Burke, Gianluigi Pillonetto
The classical approach to linear system identification is given by parametric Prediction Error Methods (PEM). In this context, model complexity is often unknown so that a model ord…
Optimization viewpoint on Kalman smoothing, with applications to robust and sparse estimation
Aleksandr Y. Aravkin, James V. Burke, Gianluigi Pillonetto
In this paper, we present the optimization formulation of the Kalman filtering and smoothing problems, and use this perspective to develop a variety of extensions and applications.…
Kalman smoothing and block tridiagonal systems: new connections and numerical stability results
Aleksandr Y. Aravkin, Bradley B. Bell, James V. Burke +1
The Rauch-Tung-Striebel (RTS) and the Mayne-Fraser (MF) algorithms are two of the most popular smoothing schemes to reconstruct the state of a dynamic linear system from measuremen…
Convex vs nonconvex approaches for sparse estimation: GLasso, Multiple Kernel Learning and Hyperparameter GLasso
Aleksandr Y. Aravkin, James V. Burke, Alessandro Chiuso +1
The popular Lasso approach for sparse estimation can be derived via marginalization of a joint density associated with a particular stochastic model. A different marginalization of…